Structural equation modeling analysis of determinants of barriers to breast self-examination among Eastern Chinese women

Background This study aimed to establish a structural equation model to determine the associations among knowledge of breast cancer, beliefs about breast self-examination (BSE), BSE practice, socio-economic status (SES), and barriers to BSE among Eastern Chinese women. Methods An online cross-sectional correlational survey of 2026 women from Eastern China was undertaken by a self-administered questionnaire in 2020. Structural equation modeling was utilized for examining the interrelationships between BSE barriers and other variables. Results Our results suggested that BSE barriers were significantly and negatively related to SES, BSE beliefs, and BSE practices (β = -0.176, p < 0.001 and β = -0.118, p < 0.001 and β = -0.435, p < 0.001, respectively). Among them, participants’ BSE practices had the strongest effects on BSE barriers, while the effect of breast cancer knowledge on BSE barriers was in an opposite direction (β = 0.177, p < 0.001). Conclusions BSE barriers were influenced by SES, breast cancer knowledge, BSE beliefs and BSE practices. Our results warn that breast cancer prevention education should strengthen knowledge of practical methods rather than general knowledge. Therefore, intervention strategies designed to develop BSE and breast cancer prevention should take into account multiple factors, in particular finding more effective ways for the public to turn knowledge into a motivator rather than a barrier.


Introduction
Breast cancer is the first most frequent malignancy and the number five cause of cancer deaths in women globally, with 2.3 million new cases and 685 000 deaths in 2020 [1]. More than half of the cases occurred in low-and middle-income countries (LMICs). China, as the largest LMIC country, also faces the heavy burden of breast cancer. In 2020, there were approximately 420,000 breast cancer cases among Chinese women, accounting for 19.9% of total new cases worldwide [1]. Moreover, breast cancer incidence in China has increased more than twice as fast as the global rates, especially in urban areas [2]. Breast cancer has become a major healthcare challenge for China, which poses a serious threat to the health of Chinese women [3].
Fortunately, earlier detection through screening can significantly alleviate the burden of this highly treatable disease and improve survival. Evidence has shown that 25%-30% of breast cancers can be controlled through early effective screening [4]. Recommended screening strategies include clinical breast examination (CBE) by a physician, mammography, and breast self-examination (BSE) [5]. Until now, mammographic screening has been deemed to be the best approach to identifying breast cancer early [6]. However, this approach is limited in LMIC settings because of its high cost and low availability.
BSE has a unique role in the initial diagnosis of breast cancer in the asymptomatic stage, compared with other screening techniques [7]. It is an easy, cost-effective, widely available method for detecting breast cancer, which does not require a hospital visit and specialized equipment [8]. In addition, BSE is also considered an important part of breast health education. It contributes to improving women's breast health awareness, which empowers women to familiarize themselves with their breasts and detect breast abnormalities early. Harvey et al. [9] reported that more than 65% of breast lumps were detected by women themselves. Additionally, Newcomb et al. [10] found that women who performed BSE with proficiency had a decreased chance of dying from breast cancer. Consequently, BSE is of great importance in countries where breast cancer is on the rise, especially for LMIC's limited health resources [7,8]. Currently, there is no nationwide population-based screening program for breast cancer in China [11]. BSE may be considered an attainable strategy in empowering Chinese women to detect breast cancer early.
Despite the well-established benefits of BSE, many women are inactive and do not perform it regularly [12]. A study conducted in Nigeria indicated that only 5.3% of women performed BSE regularly [13]. Similar results were reported from studies in Yemen [14], and Ethiopia [15].
To implement effective promotion strategies, gaining a deeper understanding of barriers to BSE is of fundamental significance. Many studies have confirmed that lack of knowledge and weaker health beliefs are significant barriers to widespread BSE implementation encountered by women [16]. In addition, a study conducted in Nigeria reported that fear of detecting breast abnormality is an important factor affecting BSE uptake. The result of a study done among Malaysian women showed that not having enough privacy, embarrassment, unnecessary and spending too much time were also factors that influence BSE performance [17].
Low socioeconomic status (SES) is another significant barrier to BSE practice. SES is a potential determinant of health-protective behaviour, referring to an individual's position in the social structure. It is generally assessed using income, educational attainment, and occupation. Previous studies demonstrated that women with higher SES had greater odds of performing BSE than those with lower SES [18], and more highly educated women tended to have higher BSE practice rates than those less educated [19]. In addition, a study by Akinyemiju et al. [20] found that higher SES women were more likely to be aware of the need for breast screening, with lower barriers.
Knowledge, Attitude and Practice (KAP) theory is a theoretical model used to study and promote human health behaviours [21]. KAP theory suggests that health knowledge is the basis for building positive attitudes and behaviour change, attitudes or beliefs are the motivation for behaviour change, and the formation of healthy behaviours is the final goal [21]. However, some studies from China and Singapore that have applied the KAP theory have found that health knowledge and attitudes are not always effective in influencing behaviour [22,23], so we need to understand that there are limitations to the KAP theory and that it does not always fully explain changes in health behaviour. We need to consider more relevant factors. Based on KAP theory, we speculate that there is a link between barriers and other factors such as knowledge, beliefs, behaviours and SES. Therefore, we constructed a hypothetical model between breast cancer knowledge, beliefs of BSE, BSE practice, SES and barriers related to breast cancer among women in East China.
Nevertheless, the interrelationship between influencing factors and BSE barriers, and the mechanism of action, remains unclear and requires further research. In addition, knowledge, beliefs, and other factors are latent variables that are difficult to measure directly. The traditional statistical method is unable to elucidate the complex causal relationships when potential variables are concerned. Structural equation modeling (SEM) is a far superior multivariate analysis approach that compensates for the defects of the conventional statistical technique [24]. It is used to simultaneously estimate and quantify complex relationships between the observed and latent variables in a model framework [24].
This study aimed to explore the factors influencing barriers to BSE among Eastern Chinese women and the interrelationship between these factors using an SEM approach.

Participants and data collection
We conducted this cross-sectional study in Anhui Province, Eastern China. The target population was women (single and married), older than 18 years. Women diagnosed with breast cancer were excluded. We adopted the convenience sampling method to issue an anonymous online questionnaire through WeChat (a Chinese messaging app) and e-mails in 2020. All submitted questionnaires were examined and verified by the submitter to ensure that they were completed in full. Finally, 2293 questionnaires were completed. After excluding invalid questionnaires, 2026 valid questionnaires were obtained, with an efficiency rate of 88.36%.

Sample size calculation
We used a convenience sampling method to conduct this cross-sectional survey. Using the Kish Leslie formula [25], we determined the sample size required for this survey to be 423: N = Z 2 P (1-P) d 2 (Z = 95% CI = 1.96, P = 27.5%, d = 0.05). Where the P-value is the BSE practice rate and is derived from previous studies on breast cancer screening behaviour among Chinese women [26]. In addition, the minimum required sample size was 508 after taking into account a 20% non-response rate. The total sample size used in this study was 2026, which exceeded the minimum requirement. The sample size was sufficient to ensure the credibility of the study results.

Consent to participate and ethics approval
All participants volunteered to take part in the survey and were of legal age in China. All participants voluntarily clicked on the link to the online questionnaire and completed the questionnaire. Before completing the online questionnaire, all participants were informed of the purpose of the study, confidentiality of data and consent information, and provided verbal informed consent by clicking "Continue". This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Medical Ethics Committee of Bengbu Medical College (2018047).

Instruments
Most other previous questionnaires on breast cancer and BSE have focused on breast cancer knowledge, beliefs and BSE practice [27][28][29][30]. Questionnaires on SES and BSE barriers are rare. Based on an extensive review of previous studies [20,[31][32][33], A researcher-designed questionnaire was self-administered for the aims of our study and pre-tested on randomly sampled eligible participants before the formal survey. It addressed sociodemographic information, SES, breast cancer knowledge, beliefs of BSE, BSE practice and barriers to BSE. As the participants in this study were women from Eastern China, the language of the questionnaire used for the study and the language used for questionnaire administration was Chinese.
SES variables included current occupation (unemployed and retired/low-tech worker or farmer/intermediate technical worker or middle manager/senior technical worker or senior manager), education level (elementary school and below/junior high school/high school or secondary school / junior college/university/master/doctor), and annual household income (< 50,000 CNY (Chinese yuan renminbi)/50,000-120,000 CNY/ > 120,000 CNY).
Breast cancer knowledge was measured using 22 items, regarding risk factors, symptoms, screening methods and treatment strategies. Each item was rated as "true" (1 point) or "false" (0 points). Higher scores (range, 0-22) indicated better knowledge.
Seven items were used to assess the participants' beliefs about BSE, for example, "It's easy for me to keep up with regular breast exams or hospital visits", "I believe I can handle unexpected situations during inspections" and so on. The responses were scored using a five-point scale ranging from 0 (strongly disagree) to 4 (strongly agree). High scores (range, 0-28) reflected a high-level belief of BSE.
The question "Do you have ever done BSE?" was first used to estimate BSE practice. Then those who had performed BSE were asked about the BSE frequency, techniques, and examination content. The responses to the BSE frequency were scored ranging from 1 (annually) to 4 (monthly). Response options for the other 3 questions were "No = 0" or "Yes = 1". Higher scores (range, 1-7) represented better BSE practices.
Barriers to BSE were assessed using 5 items, for example, "I would be embarrassed to do a breast self-examination". Each item had two options, which are "No" and "Yes" with scores of 0, and 1 respectively. Higher scores (range, 0-5) represented better BSE practices. A higher score of barriers to BSE (range, 0-5) indicated the significant likelihood of not participating in BSE implementation.
Cronbach's alpha for this questionnaire was 0.814, KMO (Kaiser-Meyer-Olkin) was 0.825, and the P-value was 0.001, showing good reliability and validity.

Data analysis
Data analysis was performed using statistical software IBM SPSS Statistics 26.0 and IBM SPSS Amos 24.0. The significance level was set at a P-value < 0.05.
Descriptive statistics were adopted to summarize the demographic information of the study sample. The overall mean of continuous variables (e.g., age) and the proportional distribution of categorical variables (e.g., ethnicity, marital status) were calculated to describe the whole independent sample. T-tests or analysis of variance were applied to compare the mean difference between the groups. Spearman's correlation analysis was conducted to evaluate correlations between major variables. The SEM was employed to determine the relationships among study variables. Skewness and kurtosis tests were performed to assess the normality of observed data. The maximum likelihood estimation method was applied for parameter estimation.

Indicators of model fit
A variety of goodness-of-fit indicators were applied in this study to evaluate the model fit of the hypothetical model. We used the chi-square value to degrees of freedom (χ 2 /df). χ 2 /df less than 5 is a sign of an overall acceptable model fit. We also used the comparative fit index (CFI) to evaluate the fit of the model. When the CFI is above 0.95, the model fit is good, with values below 0.9 indicating a poor fit. In addition, the goodness of fit index (GFI), Adjusted GFI (AGFI), adjusted goodness of fit index (RMSEA) and other fitness indices are also used to assess the model fitting.

Modifications to the model
To get a better fit of the model, we used the modified indices method for the model modifications. The modified indices method is a way to improve the model by identifying parameters. The removal of insignificant paths (p > 0.05) and the addition of potentially significant ones are shown in the modification indices test. Nevertheless, our application of the modification indices was theory-based. It is important to avoid adding paths in a fully data-driven manner, as this is likely to lead to over-fitting.

Characteristics of participants
The mean age of the 2,026 participants was 32.30 ± 12.204 years. Among them, 30.11% were over 40 years old and 47.38% were married. 17.08% of the participants had a history of breast disease. Over two-thirds of the participants had a university degree or higher. 45.31% of participants had annual household incomes between 50,000 CNY and RMB 120,000. 31.29% of participants had annual household incomes over 120,000 CNY. Univariate analyses indicated that breast cancer knowledge, beliefs of BSE, BSE practice and barriers to BSE were associated with most sociodemographic variables. Other demographic data are presented in Table 1.

Structural equation modeling analysis
As a result, all measured variables showed a normal distribution (Skewness < 3, Kurtosis < 8). The results of the SEM indicated the original hypothesized model (M1) did not fit the data well. According to modification indices and the theoretical background of AMOS, we concatenated some error terms of the same potential variables. After model modification, we obtained the final SEM model (M2) with the better fitting degree (Fig 2). Table 3 summarizes the fitness index of the SEM model M1 and model M2.
Moreover, the standardized path coefficients of the model M2 are statistically significant (all p < 0.01). The results of the path analysis indicated that participants' barriers to BSE were directly and significantly negatively associated with SES (β = -0.176, p < 0.001), beliefs of BSE (β = -0.118, p < 0.001) and BSE practice (β = -0.435, p < 0.001), and was also positively related to breast cancer knowledge (β = 0.177, p < 0.001). Besides, BSE practice was directly correlated with SES (β = 0.329, p < 0.001), Breast cancer knowledge (β = 0.109, p < 0.001) and Beliefs of BSE (β = 0.202, p < 0.001). There was also a significant positive relationship between SES and  Here BSE practice was strongly associated with BSE barriers. Our research showed that participants with good BSE habits had lower barriers to BSE. BSE practice might increase women's perceived motivation, positive beliefs and awareness of the benefits of BSE, as well as reduce the resistance and fear of BSE. They had a deeper understanding of the necessity of BSE and the dangers of breast cancer, and thus had lower barriers to BSE. In the study on BSE practice from University Tabuk in Saudi Arabia, similar views to ours were also mentioned [34]. A related study from Turkey also confirmed the role of BSE practices in enhancing women's selfefficacy, increasing health motivation and reducing perceived barriers to mammography [35].
SES had a significant impact on BSE barriers and also a strong association with BSE practice and breast cancer knowledge. Our study showed that participants with higher SES had lower BSE barriers. One of the potential explanations for this phenomenon was that participants of higher social status, or with higher levels of education or higher income, were more aware of the dangers of breast cancer and more willing to undertake BSE for their health. From our results, we knew that civil servants, scientific researchers, medical workers or well-educated participants had a lower barrier to BSE and paid more attention to breast cancer. The research results of Madubogwu et al. [36] and Taleghani et al. [37] were also consistent with our findings. In addition, participants with higher household incomes spent more on healthcare and had greater access to health literacy training and medical services. This made them more knowledgeable about breast cancer and more open to BSE, lowering their barriers to BSE. It was clear from the results of the study that participants living in poor rural areas had higher barriers to BSE than those in urban areas. In a study from southwest Cameroon, Azemfac et al. reported that socio-economic disparities may be a determining factor in the spread of BSE knowledge and barriers to women's care [38].
Another watchable result of our study was that breast cancer knowledge was significantly and positively linked to BSE barriers in participants. A study by Diyarbak from Turkey also found similar results to ours [39]. One of the underlying causes of this phenomenon was probably that breast cancer knowledge was not sufficient to reduce participants' barriers to BSE in the absence of BSE practice. Our results indicated that only a small percentage of participants had regular BSE (17.7%). Perhaps this poor practice situation influenced the effect of breast cancer knowledge on participants' BSE barriers. In addition, we found that the participants lacked knowledge of BSE, with 52.3% of the participants not knowing the correct examination methods. The lack of specific guidance information to motivate participants may be one of the reasons for this phenomenon. A study by X-L Gao et al. suggested that specific professional health knowledge had a greater impact on people's health than general health knowledge [22].
Our model also suggested that positive BSE beliefs had a significantly negative impact on BSE barriers. Positive health beliefs can diminish participants' fear, embarrassment resistance, and other negative psychology towards BSE. A survey of Malaysian women indicated that 61.5% of respondents had worries about BSE [17]. Many previous studies had indicated that fear, anxiety, and embarrassment were nonnegligible components of BSE barriers [6,40]. This highlighted the importance of health belief factors in diminishing BSE barriers.
Our study is not flawless, and it still has some limitations as follows. The main limitation stemmed from our use of SEM, which made us cannot derive causality in the study. Notably, our participants are all from Anhui Province, China, which may cause our study to be non- nationally representative. In addition, there may be some flaws in the questionnaire design. In the more in-depth studies, we should conduct better longitudinal designs and expand sample sizes to address the above limitations.

Conclusions
In conclusion, our study focused on the factors influencing BSE barriers among women in East China. BSE barriers were significantly linked with breast cancer knowledge, BSE belief, BSE practice, and SES. BSE practice had a stronger effect on BSE barriers. SES had a strong connection with breast cancer knowledge and BSE practice. Moreover, in general, good breast cancer knowledge can reduce BSE barriers. However, in our model, breast cancer knowledge had a stimulative effect on BSE barriers. These results suggested that prospective prevention of breast cancer should be more comprehensive and take into account multiple factors. In the dissemination of prevention knowledge, we may need to focus more on practical knowledge rather than general knowledge. It is imperative to find a more efficacious method for people to turn their knowledge into a motivator rather than a barrier.